Dataset 1: New York City Leading Causes of Death

DataSet Information:

The leading causes of death by sex and ethnicity in New York City since 2007.

The data can be downloaded from https://data.cityofnewyork.us/api/views/jb7j-dtam/rows.csv?accessType=DOWNLOAD

Attribute Information:

Year
Leading.Cause
Sex
Race.Ethnicity
Deaths
Death.Rate
Age.Adjusted.Death.Rate

Load the file from the URL.

require(dplyr)

## Loading required package: dplyr

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

require(tidyr)

## Loading required package: tidyr

require(knitr)

## Loading required package: knitr

require(ggplot2)

## Loading required package: ggplot2

nyc_data<-read.csv("https://data.cityofnewyork.us/api/views/jb7j-dtam/rows.csv?accessType=DOWNLOAD", sep = ",")

str(nyc_data)

## 'data.frame':    1094 obs. of  7 variables:
##  $ Year                   : int  2014 2011 2008 2010 2012 2007 2011 2007 2012 2009 ...
##  $ Leading.Cause          : Factor w/ 26 levels "Accidents Except Drug Posioning (V01-X39, X43, X45-X59, Y85-Y86)",..: 12 7 19 19 7 21 2 10 14 13 ...
##  $ Sex                    : Factor w/ 2 levels "F","M": 1 2 2 1 2 2 1 1 1 2 ...
##  $ Race.Ethnicity         : Factor w/ 6 levels "Asian and Pacific Islander",..: 5 6 4 3 2 4 4 2 3 3 ...
##  $ Deaths                 : Factor w/ 465 levels ".","10","1007",..: 24 269 409 10 132 1 87 122 4 85 ...
##  $ Death.Rate             : Factor w/ 442 levels ".","10","10.1",..: 1 189 1 409 167 1 1 100 393 62 ...
##  $ Age.Adjusted.Death.Rate: Factor w/ 427 levels ".","10","10.1",..: 1 156 1 424 231 1 1 77 412 225 ...

nyc_data <- subset(nyc_data, nyc_data$Deaths != '.')

str(nyc_data)

## 'data.frame':    956 obs. of  7 variables:
##  $ Year                   : int  2014 2011 2008 2010 2012 2011 2007 2012 2009 2008 ...
##  $ Leading.Cause          : Factor w/ 26 levels "Accidents Except Drug Posioning (V01-X39, X43, X45-X59, Y85-Y86)",..: 12 7 19 19 7 2 10 14 13 7 ...
##  $ Sex                    : Factor w/ 2 levels "F","M": 1 2 2 1 2 1 1 1 2 1 ...
##  $ Race.Ethnicity         : Factor w/ 6 levels "Asian and Pacific Islander",..: 5 6 4 3 2 4 2 3 3 4 ...
##  $ Deaths                 : Factor w/ 465 levels ".","10","1007",..: 24 269 409 10 132 87 122 4 85 64 ...
##  $ Death.Rate             : Factor w/ 442 levels ".","10","10.1",..: 1 189 1 409 167 1 100 393 62 1 ...
##  $ Age.Adjusted.Death.Rate: Factor w/ 427 levels ".","10","10.1",..: 1 156 1 424 231 1 77 412 225 1 ...

There are 956 observations after removing rows with “.” and 7 columns.

Analysis:

1. Find the leading cause of death since 2007 for both males and female

        analysis1 <- nyc_data  %>%
      select(Year, Sex, Leading.Cause, DeathCount = ((as.numeric(as.character(Deaths)) ))) %>% 
      group_by(Year, Sex, Leading.Cause) %>% 
      summarise ( Total = sum(as.numeric(as.character(DeathCount))) )  %>%
            select(Year, Sex, Leading.Cause,Total) %>%
           arrange((Year), Sex ) %>%
           top_n(1,Total)   
           

           kable(analysis1)

Year	Sex	Leading.Cause	Total
2007	F	Diseases of Heart (I00-I09, I11, I13, I20-I51)	11743
2007	M	Diseases of Heart (I00-I09, I11, I13, I20-I51)	9698
2008	F	Diseases of Heart (I00-I09, I11, I13, I20-I51)	11605
2008	M	Diseases of Heart (I00-I09, I11, I13, I20-I51)	9587
2009	F	Diseases of Heart (I00-I09, I11, I13, I20-I51)	10780
2009	M	Diseases of Heart (I00-I09, I11, I13, I20-I51)	9304
2010	F	Diseases of Heart (I00-I09, I11, I13, I20-I51)	9463
2010	M	Diseases of Heart (I00-I09, I11, I13, I20-I51)	8464
2011	F	Diseases of Heart (I00-I09, I11, I13, I20-I51)	9083
2011	M	Diseases of Heart (I00-I09, I11, I13, I20-I51)	7817
2012	F	Diseases of Heart (I00-I09, I11, I13, I20-I51)	8777
2012	M	Diseases of Heart (I00-I09, I11, I13, I20-I51)	7954
2013	F	Diseases of Heart (I00-I09, I11, I13, I20-I51)	8745
2013	M	Diseases of Heart (I00-I09, I11, I13, I20-I51)	8014
2014	F	Diseases of Heart (I00-I09, I11, I13, I20-I51)	8556
2014	M	Diseases of Heart (I00-I09, I11, I13, I20-I51)	7961

2. Find the leading cause of death since 2007 by Race.Ethnicity

           analysis2 <- nyc_data  %>%
select(Year, Ethnicity=Race.Ethnicity, Leading.Cause, DeathCount=(as.numeric(as.character(Deaths)) )) %>% 
           group_by(Year, Ethnicity,Leading.Cause) %>% 
    summarise ( Total = sum(as.numeric(as.character(DeathCount))) )  %>%
            select(Year, Ethnicity, Leading.Cause, Total) %>%
           arrange(desc(Year), Ethnicity,Leading.Cause) %>%
           top_n(1,Total) 
           
          kable(analysis2)

Year	Ethnicity	Leading.Cause	Total
2014	Asian and Pacific Islander	Malignant Neoplasms (Cancer: C00-C97)	1159
2014	Black Non-Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	4152
2014	Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	2511
2014	Not Stated/Unknown	Diseases of Heart (I00-I09, I11, I13, I20-I51)	210
2014	Other Race/ Ethnicity	All Other Causes	133
2014	White Non-Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	8497
2013	Asian and Pacific Islander	Malignant Neoplasms (Cancer: C00-C97)	1100
2013	Black Non-Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	4233
2013	Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	2568
2013	Not Stated/Unknown	Diseases of Heart (I00-I09, I11, I13, I20-I51)	257
2013	Other Race/ Ethnicity	Diseases of Heart (I00-I09, I11, I13, I20-I51)	113
2013	White Non-Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	8620
2012	Asian and Pacific Islander	Malignant Neoplasms (Cancer: C00-C97)	1083
2012	Black Non-Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	4209
2012	Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	2514
2012	Not Stated/Unknown	Diseases of Heart (I00-I09, I11, I13, I20-I51)	175
2012	Other Race/ Ethnicity	Diseases of Heart (I00-I09, I11, I13, I20-I51)	87
2012	White Non-Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	8875
2011	Asian and Pacific Islander	Malignant Neoplasms (Cancer: C00-C97)	1001
2011	Black Non-Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	4083
2011	Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	2549
2011	Not Stated/Unknown	Diseases of Heart (I00-I09, I11, I13, I20-I51)	105
2011	Other Race/ Ethnicity	Diseases of Heart (I00-I09, I11, I13, I20-I51)	73
2011	White Non-Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	9236
2010	Asian and Pacific Islander	Malignant Neoplasms (Cancer: C00-C97)	940
2010	Black Non-Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	4297
2010	Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	2670
2010	Not Stated/Unknown	Diseases of Heart (I00-I09, I11, I13, I20-I51)	140
2010	Other Race/ Ethnicity	Diseases of Heart (I00-I09, I11, I13, I20-I51)	102
2010	White Non-Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	9846
2009	Asian and Pacific Islander	Diseases of Heart (I00-I09, I11, I13, I20-I51)	1002
2009	Black Non-Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	4603
2009	Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	2731
2009	Not Stated/Unknown	Diseases of Heart (I00-I09, I11, I13, I20-I51)	205
2009	Other Race/ Ethnicity	Diseases of Heart (I00-I09, I11, I13, I20-I51)	78
2009	White Non-Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	11465
2008	Asian and Pacific Islander	Diseases of Heart (I00-I09, I11, I13, I20-I51)	1002
2008	Black Non-Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	4802
2008	Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	2775
2008	Not Stated/Unknown	Diseases of Heart (I00-I09, I11, I13, I20-I51)	200
2008	Other Race/ Ethnicity	Diseases of Heart (I00-I09, I11, I13, I20-I51)	74
2008	White Non-Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	12339
2007	Asian and Pacific Islander	Diseases of Heart (I00-I09, I11, I13, I20-I51)	924
2007	Black Non-Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	4843
2007	Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	2745
2007	Not Stated/Unknown	Diseases of Heart (I00-I09, I11, I13, I20-I51)	168
2007	Other Race/ Ethnicity	Diseases of Heart (I00-I09, I11, I13, I20-I51)	79
2007	White Non-Hispanic	Diseases of Heart (I00-I09, I11, I13, I20-I51)	12682

3. Since heart disease was the leading cause, find the count of this disease since 2007.

        wide <- nyc_data %>% 
        select(Year, Leading.Cause, Deaths) %>% 
        filter(Leading.Cause == "Diseases of Heart (I00-I09, I11, I13, I20-I51)") %>%
        group_by(Year, Leading.Cause) %>%
        summarise ( Total = sum(as.numeric(as.character(Deaths) )  )) %>%
        spread(Year, Total)  
   
        kable(wide)

Leading.Cause	2007	2008	2009	2010	2011	2012	2013	2014
Diseases of Heart (I00-I09, I11, I13, I20-I51)	21441	21192	20084	17927	16900	16731	16759	16517

Conclusion: The disease rate is decreasing in a very slower phase.

Dataset 2: Projected Population 2010-2040 - Total By Age Groups

DataSet Information:

Projected total New York City population for five intervals from 2010 through 2040 by Borough, broken down by 18 age cohorts. (Age groups may not add up to the total due to rounding.)

The data set can be downloaded from https://data.cityofnewyork.us/api/views/97pn-acdf/rows.csv?accessType=DOWNLOAD

Attribute Information:

Borough
Age
2010
2015
2020
2025
2030
2035
2040

Load the file from the URL.

population<-read.csv("https://data.cityofnewyork.us/api/views/97pn-acdf/rows.csv?accessType=DOWNLOAD", sep = ",")

str(population)

## 'data.frame':    114 obs. of  9 variables:
##  $ Borough: Factor w/ 6 levels "Bronx","Brooklyn",..: 4 4 4 4 4 4 4 4 4 4 ...
##  $ Age    : Factor w/ 19 levels "0-4","10-14",..: 1 3 4 5 6 7 8 9 11 12 ...
##  $ X2010  : int  521990 539844 647483 736105 667657 592299 571825 570273 546204 479661 ...
##  $ X2015  : int  535209 505783 646075 770396 707726 611239 550097 535998 552074 493997 ...
##  $ X2020  : int  545778 492532 606203 763956 743916 649594 569628 517668 520597 501239 ...
##  $ X2025  : int  547336 519298 591683 715824 740268 684249 606185 537516 504322 474319 ...
##  $ X2030  : int  542426 535024 625253 698195 693684 682964 638148 571723 523815 459574 ...
##  $ X2035  : int  540523 546062 643728 740437 675497 639237 637517 600792 556586 477052 ...
##  $ X2040  : int  546426 546750 657403 762757 715486 621899 596493 600514 584164 506390 ...

This data set has 114 observations and 9 columns.

Analysis:

1. Analyze the projected population in 2020 for all 5 Boroughs in different age groups.

    head(population)

##     Borough   Age  X2010  X2015  X2020  X2025  X2030  X2035  X2040
## 1 NYC Total   0-4 521990 535209 545778 547336 542426 540523 546426
## 2 NYC Total 15-19 539844 505783 492532 519298 535024 546062 546750
## 3 NYC Total 20-24 647483 646075 606203 591683 625253 643728 657403
## 4 NYC Total 25-29 736105 770396 763956 715824 698195 740437 762757
## 5 NYC Total 30-34 667657 707726 743916 740268 693684 675497 715486
## 6 NYC Total 35-39 592299 611239 649594 684249 682964 639237 621899

# remove the rows with total in columns Borough and Age.

    analysis2.1 <- population  %>%
        select(Borough,Age,X2020 ) %>%      
         filter( Borough != "NYC Total" ) %>% 
         filter( Age != "Total" ) %>% 
         spread(Age,X2020)

         kable(analysis2.1)

Borough	0-4	10-14	15-19	20-24	25-29	30-34	35-39	40-44	45-49	5-9	50-54	55-59	60-64	65-69	70-74	75-79	80-84	85+
Bronx	109972	95703	98987	113827	127995	109797	93530	87217	83496	105775	89495	85632	73506	56988	43380	31168	20292	20028
Brooklyn	186886	166761	158561	180072	221421	221544	205525	181452	159615	180252	152501	144647	137606	113940	92570	61041	41717	42341
Manhattan	82096	60331	69487	118301	180070	190129	146480	110439	94732	68243	94242	88669	84257	76334	64227	47159	31115	31971
Queens	138141	129351	134198	162692	200730	189015	172928	160306	149550	132342	151019	147504	137219	107634	82663	57287	38275	39441
Staten Island	28683	30307	31299	31311	33740	33431	31131	30214	30275	29394	33340	34787	31599	25968	21232	13581	8259	8604

2. Analyze the projected percentage change in population between 2020 and 2040 for all 5 Boroughs.

    analysis2.2 <- population  %>%
        select(Borough,Age,X2020,X2040 ) %>%      
         filter( Borough != "NYC Total" ) %>% 
         filter( Age == "Total" ) %>% 
         select(Borough,X2020,X2040) %>% 
          mutate(Pct.Change = round(((X2040 - X2020)/X2040) * 100, 2)) %>%
           rename(Year_2020=X2020, Year_2040=X2040) 
       
           kable(analysis2.2)

Borough	Year_2020	Year_2040	Pct.Change
Bronx	1446788	1579245	8.39
Brooklyn	2648452	2840525	6.76
Manhattan	1638282	1691617	3.15
Queens	2330295	2412649	3.41
Staten Island	487155	501109	2.78

Dataset 3: SAT Results

DataSet Information:

The most recent school level results for New York City on the SAT. Results are available at the school level for the graduating seniors of 2012. Records contain 2012 College-bound seniors mean SAT scores taken during SY 2012.

The data set can be downloaded from the below URL: https://data.cityofnewyork.us/api/views/f9bf-2cp4/rows.csv?accessType=DOWNLOAD

Attribute Information:

DBN
SCHOOL NAME
Num of SAT Test Takers
SAT Critical Reading Avg. Score
SAT Math Avg. Score
SAT Writing Avg. Score

Load the file from the URL.

sat_result<-read.csv("https://data.cityofnewyork.us/api/views/f9bf-2cp4/rows.csv?accessType=DOWNLOAD", sep = ",")

str(sat_result)

## 'data.frame':    478 obs. of  6 variables:
##  $ DBN                            : Factor w/ 478 levels "01M292","01M448",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ SCHOOL.NAME                    : Factor w/ 478 levels "47 THE AMERICAN SIGN LANGUAGE AND ENGLISH SECONDARY SCHOOL",..: 204 438 141 169 309 296 333 110 38 1 ...
##  $ Num.of.SAT.Test.Takers         : Factor w/ 175 levels "10","101","102",..: 68 166 136 135 98 11 35 42 20 36 ...
##  $ SAT.Critical.Reading.Avg..Score: Factor w/ 164 levels "279","287","300",..: 34 62 56 93 69 17 145 95 159 74 ...
##  $ SAT.Math.Avg..Score            : Factor w/ 173 levels "312","315","317",..: 70 87 68 67 94 153 158 82 164 66 ...
##  $ SAT.Writing.Avg..Score         : Factor w/ 163 levels "286","291","297",..: 45 48 52 41 66 11 147 92 158 69 ...

Tidy the data set.

# remove the rows that does not have scores
sat_result1 <- sat_result %>% 
               filter(Num.of.SAT.Test.Takers != 's' )

str(sat_result1)

## 'data.frame':    421 obs. of  6 variables:
##  $ DBN                            : Factor w/ 478 levels "01M292","01M448",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ SCHOOL.NAME                    : Factor w/ 478 levels "47 THE AMERICAN SIGN LANGUAGE AND ENGLISH SECONDARY SCHOOL",..: 204 438 141 169 309 296 333 110 38 1 ...
##  $ Num.of.SAT.Test.Takers         : Factor w/ 175 levels "10","101","102",..: 68 166 136 135 98 11 35 42 20 36 ...
##  $ SAT.Critical.Reading.Avg..Score: Factor w/ 164 levels "279","287","300",..: 34 62 56 93 69 17 145 95 159 74 ...
##  $ SAT.Math.Avg..Score            : Factor w/ 173 levels "312","315","317",..: 70 87 68 67 94 153 158 82 164 66 ...
##  $ SAT.Writing.Avg..Score         : Factor w/ 163 levels "286","291","297",..: 45 48 52 41 66 11 147 92 158 69 ...

# split the DBN column to School district and code
school_District <- substring(sat_result1$DBN,1,3 )
school_code <- substring(sat_result1$DBN,4,6 )

#change the name of the columns
names(sat_result1)[3:6] <- c('No_of_SAT_takers','Reading_Score','Math_Score','Writing_Score')

sat_result2 <- sat_result1 %>%
  mutate(school_District, school_code) %>%
  select(school_District, school_code,SCHOOL.NAME,No_of_SAT_takers,Reading_Score,Math_Score,Writing_Score )

str(sat_result1)

## 'data.frame':    421 obs. of  6 variables:
##  $ DBN             : Factor w/ 478 levels "01M292","01M448",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ SCHOOL.NAME     : Factor w/ 478 levels "47 THE AMERICAN SIGN LANGUAGE AND ENGLISH SECONDARY SCHOOL",..: 204 438 141 169 309 296 333 110 38 1 ...
##  $ No_of_SAT_takers: Factor w/ 175 levels "10","101","102",..: 68 166 136 135 98 11 35 42 20 36 ...
##  $ Reading_Score   : Factor w/ 164 levels "279","287","300",..: 34 62 56 93 69 17 145 95 159 74 ...
##  $ Math_Score      : Factor w/ 173 levels "312","315","317",..: 70 87 68 67 94 153 158 82 164 66 ...
##  $ Writing_Score   : Factor w/ 163 levels "286","291","297",..: 45 48 52 41 66 11 147 92 158 69 ...

This data set has 421 observations and 6 columns.

1. Rank the schools by math score, reading and writing score. Find the top 10 schools.

sat_result2$school_District = as.factor(sat_result2$school_District)
sat_result2$Reading_Score = as.numeric(sat_result2$Reading_Score)
sat_result2$Math_Score  = as.numeric(sat_result2$Math_Score )
sat_result2$Writing_Score = as.numeric(sat_result2$Writing_Score)

school_Rank<-round(rank( -(sat_result2$Reading_Score+sat_result2$Math_Score+sat_result2$Writing_Score )))

sat_result3 <- sat_result2 %>%
  mutate(school_Rank) %>%
  select(school_District, school_code,SCHOOL.NAME,No_of_SAT_takers,Reading_Score,Math_Score,Writing_Score,school_Rank ) %>%
  arrange(school_Rank) %>%
  top_n(-10,school_Rank)

kable(sat_result3)

school_District	school_code	SCHOOL.NAME	No_of_SAT_takers	Reading_Score	Math_Score	Writing_Score	school_Rank
02M	475	STUYVESANT HIGH SCHOOL	832	163	172	162	1
10X	445	BRONX HIGH SCHOOL OF SCIENCE	731	160	171	161	2
31R	605	STATEN ISLAND TECHNICAL HIGH SCHOOL	227	161	170	159	3
10X	696	HIGH SCHOOL OF AMERICAN STUDIES AT LEHMAN COLLEGE	92	162	165	159	4
25Q	525	TOWNSEND HARRIS HIGH SCHOOL	278	158	166	160	5
28Q	687	QUEENS HIGH SCHOOL FOR THE SCIENCES AT YORK COLLEGE	121	157	169	157	6
01M	696	BARD HIGH SCHOOL EARLY COLLEGE	130	159	164	158	7
05M	692	HIGH SCHOOL FOR MATHEMATICS, SCIENCE AND ENGINEERING AT CITY COLLEGE	101	156	167	155	8
13K	430	BROOKLYN TECHNICAL HIGH SCHOOL	1277	155	168	154	9
02M	416	ELEANOR ROOSEVELT HIGH SCHOOL	127	152	163	156	10

2. Find the Top5 school district by aggregating the math score, reading and writing score.

Total_score = (sat_result2$Reading_Score + sat_result2$Math_Score + sat_result2$Writing_Score)

sat_result4 <- sat_result2 %>%
 select( school_District, Reading_Score,Math_Score ,Writing_Score  ) %>%
  mutate(Total_score) %>%  
   group_by ( school_District)  %>% 
      summarise(Avg_Reading_Score = round(mean(Reading_Score)), Avg_math_Score = round(mean(Math_Score)), Avg_writing_Score=round(mean(Writing_Score)), Totals = round(mean(Total_score))) %>%
       top_n(5,Totals)


kable(sat_result4)

school_District	Avg_Reading_Score	Avg_math_Score	Avg_writing_Score	Totals
22K	114	119	114	347
26Q	112	121	113	346
30Q	98	117	98	313
31R	112	106	106	324
75Q	104	103	114	321

Raghu_607_Project2

Raghu Ramnath

3/11/2017

Dataset 1: New York City Leading Causes of Death

DataSet Information:

Attribute Information:

Load the file from the URL.

Analysis:

1. Find the leading cause of death since 2007 for both males and female

2. Find the leading cause of death since 2007 by Race.Ethnicity

3. Since heart disease was the leading cause, find the count of this disease since 2007.

Conclusion: The disease rate is decreasing in a very slower phase.

Dataset 2: Projected Population 2010-2040 - Total By Age Groups

DataSet Information:

Attribute Information:

Load the file from the URL.

Analysis:

1. Analyze the projected population in 2020 for all 5 Boroughs in different age groups.

2. Analyze the projected percentage change in population between 2020 and 2040 for all 5 Boroughs.

Dataset 3: SAT Results

DataSet Information:

Attribute Information:

Load the file from the URL.

Tidy the data set.

1. Rank the schools by math score, reading and writing score. Find the top 10 schools.

2. Find the Top5 school district by aggregating the math score, reading and writing score.

school_District	Avg_Reading_Score	Avg_math_Score	Avg_writing_Score	Totals
22K	114	119	114	347
26Q	112	121	113	346
30Q	98	117	98	313
31R	112	106	106	324
75Q	104	103	114	321

school_District	Avg_Reading_Score	Avg_math_Score	Avg_writing_Score	Totals
22K	114	119	114	347
26Q	112	121	113	346
30Q	98	117	98	313
31R	112	106	106	324
75Q	104	103	114	321

school_District	Avg_Reading_Score	Avg_math_Score	Avg_writing_Score	Totals
22K	114	119	114	347
26Q	112	121	113	346
30Q	98	117	98	313
31R	112	106	106	324
75Q	104	103	114	321